相关论文: Machine Learning of Interstellar Chemical Inventor…
Abridged: We detail and benchmark two sophisticated chemical models developed by the Heidelberg and Bordeaux astrochemistry groups. The main goal of this study is to elaborate on a few well-described tests for state-of-the-art astrochemical…
This paper presents the first results from a model for chemical evolution that can be applied to N-body cosmological simulations and quantitatively compared to measured stellar abundances from large astronomical surveys. This model…
Water is observed throughout the universe, from diffuse interstellar clouds to protoplanetary disks around young stars, and from comets in our own solar system and exoplanetary atmospheres to galaxies at high redshifts. This review…
Machine Learning is a powerful tool to reveal and exploit correlations in a multi-dimensional parameter space. Making predictions from such correlations is a highly non-trivial task, in particular when the details of the underlying dynamics…
We present an innovative of artificial intelligence with column chromatography, aiming to resolve inefficiencies and standardize data collection in chemical separation and purification domain. By developing an automated platform for precise…
In order to understand how molecular clouds form in the Galactic interstellar medium, we would like to be able to map the structure and kinematics of the gas flows responsible for forming them. However, doing so is observationally…
The answers to many unsolved problems lie in the intractable chemical space of molecules and materials. Machine learning techniques are rapidly growing in popularity as a way to compress and explore chemical space efficiently. One of the…
Observations of Milky Way stars by multiplexed spectroscopic instruments and of gas in nearby galaxies using integral field units have made it possible to measure the abundances of multiple elements in both the interstellar medium and the…
The concept of molecular similarity appears in many machine-learning algorithms based on the assumption that molecules with similar representations will also share similar properties. In this work, we propose a new way to study similarity…
The elemental depletion of interstellar sulfur from the gas phase has been a recurring challenge for astrochemical models. Observations show that sulfur remains relatively non-depleted with respect to its cosmic value throughout the diffuse…
Context. Interstellar dust particles, which represent 1% of the total mass, are recognized to be very powerful interstellar catalysts in star-forming regions. The presence of dust can have a strong impact on the chemical composition of…
The chemical composition of a star's atmosphere reflects the chemical composition of its birth environment. Therefore, it should be feasible to recognize stars born together that have scattered throughout the galaxy, solely based on their…
The goal of most materials discovery is to discover materials that are superior to those currently known. Fundamentally, this is close to extrapolation, which is a weak point for most machine learning models that learn the probability…
The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM)…
Predicting and enhancing inherent properties based on molecular structures is paramount to design tasks in medicine, materials science, and environmental management. Most of the current machine learning and deep learning approaches have…
Methods based on machine learning have recently made substantial inroads in many corners of cosmology. Through this process, new computational tools, new perspectives on data collection, model development, analysis, and discovery, as well…
Diffuse and translucent molecular clouds fill a vast majority of the interstellar space in the galactic disk being thus the most typical objects of the Interstellar Medium (ISM). Recent advances in observational techniques of modern optical…
Chemical imaging provides information about the distribution of chemicals within a target. When combined with structural information about the target, in situ chemical imaging opens the door to applications ranging from tissue…
(Abridged) We use a combination of high resolution N-body simulations and semi-analytic techniques to follow the formation, the evolution and the chemical enrichment of cluster galaxies in a Lambda-CDM Universe. We model the transport of…
The detection of complex organic molecules (COMs) toward dense, collapsing prestellar cores has sparked interest in the fields of astrochemistry and astrobiology, yet the mechanisms for COM formation are still debated. It was originally…